聚类网络干扰下的个体化策略评估与学习
Individualized Policy Evaluation and Learning under Clustered Network Interference
AI总结：
针对忽略网络干扰导致策略评估有偏的问题，提出一种基于半参数加性结构模型的方法，用于聚类网络干扰下的个体化处理规则评估与学习，并开发了高效且双重稳健的估计器以提升学习策略性能。
摘要：
尽管目前已有大量关于策略评估与学习的文献，但先前的大部分工作假设一个单元的处理分配不会影响另一个单元的结果。不幸的是，忽略干扰可能导致有偏的策略评估和无效的学习策略。例如，对拥有许多朋友的影响力个体进行处理可以产生正向溢出效应，从而提升个体化处理规则（ITR）的整体表现。我们考虑在聚类网络干扰（也称为部分干扰）下评估和学习最优 ITR 的问题，其中单元簇从总体中采样，且单元在每簇内可能相互影响。与先前对溢出效应施加强限制（如匿名干扰）的方法不同，提出的方法仅假设一个半参数结构模型，其中每个单元的结果是簇内个体处理的加性函数。在此模型下，我们提出了一种估计器，可用于评估 ITR 的经验性能。我们证明该估计器比未对溢出效应施加任何假设的标准逆概率加权估计器高效得多。我们推导了学习到的 ITR 的有限样本遗憾界限，表明使用我们的高效评估估计器能提升学习策略的性能。我们考虑了实验研究和观察研究，对于后者，我们开发了一种双重稳健估计器，该估计器具有半参数有效性并能产生最优遗憾界限。最后，我们进行了模拟和实证研究以说明所提方法的优势。
英文摘要：
Although there is now a large literature on policy evaluation and learning, much of the prior work assumes that the treatment assignment of one unit does not affect the outcome of another unit. Unfortunately, ignoring interference can lead to biased policy evaluation and ineffective learned policies. For example, treating influential individuals who have many friends can generate positive spillover effects, thereby improving the overall performance of an individualized treatment rule (ITR). We consider the problem of evaluating and learning an optimal ITR under clustered network interference (also known as partial interference), where clusters of units are sampled from a population and units may influence one another within each cluster. Unlike previous methods that impose strong restrictions on spillover effects, such as anonymous interference, the proposed methodology only assumes a semiparametric structural model, where each unit's outcome is an additive function of individual treatments within the cluster. Under this model, we propose an estimator that can be used to evaluate the empirical performance of an ITR. We show that this estimator is substantially more efficient than the standard inverse probability weighting estimator, which does not impose any assumption about spillover effects. We derive the finite-sample regret bound for a learned ITR, showing that the use of our efficient evaluation estimator leads to the improved performance of learned policies. We consider both experimental and observational studies, and for the latter, we develop a doubly robust estimator that is semiparametrically efficient and yields an optimal regret bound. Finally, we conduct simulation and empirical studies to illustrate the advantages of the proposed methodology.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
